Epidemic contact tracing via communication traces
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 35480166
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0095133
- Title of journal
- PLoS ONE
- Article number
- e95133
- First page
- -
- Volume
- 9
- Issue
- 5
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- While traditional contact tracing (CT) relies on knowledge of the physical proximity network, this is costly to obtain. This paper proposes an automated dual epidemic model, where infection spreads on a physical interpersonal network, which can never fully be recovered, and CT occurs on the communication network, obtained via a phone’s history. Running thousands of simulations over real interaction and communication traces obtained using mobile phones, we demonstrate the viability of our methods to arrest contagious outbreaks. Work led to an invited presentation at a Cambridge Networks Network which led to a collaboration with Prof Mascolo’s group, CS Cambridge.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -